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Decision modelling of non-pharmacological interventions for individuals with dementia: a systematic review of methodologies

Overview of attention for article published in Health Economics Review, March 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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11 X users
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1 Facebook page
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1 Redditor

Citations

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43 Mendeley
Title
Decision modelling of non-pharmacological interventions for individuals with dementia: a systematic review of methodologies
Published in
Health Economics Review, March 2018
DOI 10.1186/s13561-018-0192-8
Pubmed ID
Authors

Elizaveta Sopina, Jan Sørensen

Abstract

The main objective of this study is to conduct a systematic review to identify and discuss methodological issues surrounding decision modelling for economic evaluation of non-pharmacological interventions (NPIs) in dementia. A systematic search was conducted for publications using decision modelling to investigate the cost-effectiveness of NPIs for individuals with dementia. Search was limited to studies in English. Studies were excluded if they evaluated interventions aimed only at caregivers of patients with dementia, or if they only included economic evaluation alongside an RCT without additional modelling. Two primary, five secondary and three tertiary prevention intervention studies were identified and reviewed. Five studies utilised Markov models, with others using discrete event, regression-based simulation, and decision tree approaches. A number of challenging methodological issues were identified, including the use of MMSE-score as the main outcome measure, limited number of strategies compared, restricted time horizons, and limited or dated data on dementia onset, progression and mortality. Only one of the three tertiary prevention studies explicitly considered the effectiveness of pharmacological therapies alongside their intervention. Economic evaluations of NPIs in dementia should utilise purposefully-developed decision models, and avoid models for evaluation of pharmaceuticals. Broader outcome measures could be a way to capture the wide impact of NPIs for dementia in future decision models. It is also important to account for the effects of pharmacological therapies alongside the NPIs in economic evaluations. Access to more localised and up-to-date data on dementia onset, progression and mortality is a priority for accurate prediction.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 21%
Student > Ph. D. Student 8 19%
Student > Bachelor 5 12%
Other 3 7%
Student > Doctoral Student 2 5%
Other 5 12%
Unknown 11 26%
Readers by discipline Count As %
Medicine and Dentistry 8 19%
Nursing and Health Professions 5 12%
Business, Management and Accounting 3 7%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Psychology 3 7%
Other 9 21%
Unknown 12 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2018.
All research outputs
#4,466,075
of 23,028,364 outputs
Outputs from Health Economics Review
#74
of 436 outputs
Outputs of similar age
#87,642
of 330,380 outputs
Outputs of similar age from Health Economics Review
#2
of 7 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 436 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,380 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.